Real-Time Sensor Data Fusion for Lower Storage and Compute Load
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Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by correlating and fusing sensor data before storage, leading to excessive computational and storage requirements, and they do not generate new datasets that enhance sensor accuracy or predict future events.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor to curate, link, fuse, and validate sensor data in real-time, creating a unique dataset by correlating data before storage, thereby reducing computational and storage demands and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is stored before fusion, then data availability is improved, but storage requirements and computational load increase excessively
Solution Approach 1:
The system performs data fusion before storage by implementing a data fusion engine that correlates sensor data from multiple sources in real-time, creating fused datasets that are then stored. This preliminary fusion action reduces the volume of data requiring storage while maintaining information availability, directly resolving the contradiction between data availability and storage requirements
2Speed
If sensor data fusion is performed without correlation, then processing speed is improved, but data accuracy and actionability decrease
Solution Approach 1:
The system implements a correlation engine that continuously compares and correlates data from multiple sensor sources, using feedback loops to refine data accuracy. The correlation process validates sensor readings against each other in real-time, maintaining high processing speed while improving measurement precision through cross-validation and error correction mechanisms
3Adaptability or versatility
If multiple sensors are used without fusion, then sensor coverage is improved, but system complexity increases
Solution Approach 1:
The system merges data from multiple heterogeneous sensors through a unified data fusion engine that correlates and integrates information from diverse sensor sources. This combining approach maintains comprehensive sensor coverage and versatility while reducing system complexity by providing a standardized interface and unified processing pipeline for all sensor inputs
4Loss of time
If real-time data fusion is implemented, then response time is improved, but power consumption increases
Solution Approach 1:
The system implements selective data fusion that processes only the most critical and relevant sensor data in real-time, rather than fusing all available data continuously. This partial action approach maintains fast response times for important parameters while reducing overall computational load and power consumption by filtering out less critical data streams
Data Source
AI summary
Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.